Techniques for determining automated insulin delivery dosages

The present disclosure generally relates to automated insulin delivery processes, and, more particularly, to processes for automated insulin delivery to treat diabetes.

Diabetes mellitus is a serious medical condition caused by an inability to adequately control blood glucose levels. Typical treatments involve injecting affected individuals with the hormone insulin in an attempt to maintain blood glucose values within a desired, healthy range. Type 1 diabetes mellitus (T1D) results from an autoimmune response in which the immune system attacks pancreatic beta cells so that they no longer produce insulin. For type 2 diabetes mellitus (T2D), the pancreas may produce insulin, but it is either not a sufficient amount and/or the body's cells do not adequately respond to the insulin.

Treatment advances for T1D patients have provided for automatic insulin delivery (AID) systems to control patient insulin levels. For example, an AID system may include a wearable insulin pump that operates to automatically inject insulin into the patient periodically or based on an event (for instance, user input, a determination that the patient blood sugar is below a threshold value, and/or the like). The dosage of insulin injected via the AID system may be determined based on historical information, blood glucose information measured using AID system sensors, and/or other factors (for instance, weight, ketones, manual information (for example, the patient is having a meal or the patient is exercising)), and/or the like.

Patients with advanced T2D also require regular insulin infusion. However, conventional treatments for T2D patients generally involve manual blood glucose measurements (for instance, finger-stick measurements) and needle injections performed by the patient. Conventional AID systems, which have been designed to treat T1D patients, are not able to directly operate for T2D patients because glucose metabolism and insulin kinetics are significantly different for T2D patients compared with T1D patients. Accordingly, it would be beneficial and advantageous to have a system, a device and/or a technique for safely and effectively providing automated delivery methods for providing insulin to T2D patients.

The drawings are not necessarily to scale. The drawings are merely representations, not intended to portray specific parameters of the disclosure. The drawings are intended to depict example embodiments of the disclosure, and therefore should not be considered as limiting in scope. In the drawings, like numbering represents like elements DETAILED DESCRIPTION

The described technology generally relates to an insulin infusion process for automatically infusing a patient with insulin. In some embodiments, an insulin infusion process may be used with additional processes, algorithms, or computer applications that manage blood glucose levels and/or other forms of insulin therapy. Such processes or algorithms may generally be referred to as an “artificial pancreas” (AP) system or application or an automatic insulin delivery (AID) system or application that may operate to provide automatic delivery of insulin. In some embodiments, the automatic delivery of insulin may be based, at least in part, on blood glucose information. In some embodiments, the blood glucose information may be obtained via sensor input, such as data measured via a continuous glucose monitor (CGM) device or sensor and/or from manual measurement (for instance, via a finger-stick measurement manually entered by a user).

Conventional AID systems have generally been developed to deliver insulin based on the needs of patients with Type 1 diabetes (T1D). However, patients with advanced Type 2 diabetes (T2D) also require regular insulin infusion and, accordingly, would benefit from an AID system that is tailored specifically to the needs of T2D patients that is efficient, accurate, and does not require complex intervention, such as manual estimations of blood glucose levels and/or insulin needs. There is significant difficulty in designing an optimal AID system for T2D patients using existing technology because, among other reasons, glucose metabolism, insulin kinetics, and other disease characteristics of T2D patients are significantly different than those for T1D patients. Therefore, AID systems developed to manage T1D cannot be directly transferred to treating T2D patients.

Accordingly, some embodiments may provide processes, devices, techniques, methods, and/or other technology for operating an AID system for implementing insulin therapy to treat T2D patients. For example, some embodiments may provide an insulin delivery process configured to utilize multiple daily injection (MDI) recommendations for T2D patients operative to provide a safe, accurate, and effective AID system that can reduce the burden of insulin delivery for T2D patients. Although T2D treatments and T2D patients may be used in examples in the present disclosure, embodiments are not so limited, as the insulin delivery processes of the present disclosure may be used for T1D and/or other conditions according to some embodiments.

A T2D patient typically receives basal insulin delivery via MDI (however, without compensation from bolus insulin for excursions, such as meals or food ingestions). Accordingly, some embodiments may provide an insulin delivery process operative to convert MDI therapy information for T2D patients into continuous subcutaneous insulin infusion (CSII) information, such as CSII rates for use with an AID system. In various embodiments, an insulin delivery process may be operative to set maximum additional insulin delivery based, for example, on differences between mean blood glucose and target blood glucose information. In exemplary embodiments, an insulin delivery process may be operative to determine and adjust safety constraints for providing insulin to T2D patients via an AID system. For example, insulin delivery processes may be configured to determine, adjust, or tune safety constraints to avoid hypoglycemia specifically for T2D patients, for example, accounting for the different characteristics and lower risks of hypoglycemia for T2D patients compared with T1D patients. In various embodiments, an insulin delivery process may be operative to deliver insulin using an AID system based on finger-stick blood glucose measurements (for example, instead of or in addition to CGM measurements).

Therefore, insulin infusion processes according to some embodiments may provide multiple technological advantages and technical features over conventional systems, including improvements to computing technology. One non-limiting example of a technological advantage may include determining AID processes based, at least in part, on conventional, manual T2D treatment information (for instance, MDI and/or total daily insulin (TDI) information). In this manner, T2D patients may be able to utilize automated processes and devices for treating T2D, experiencing the same or similar advantages for treatment, user experience, convenience, accuracy, efficiency, and/or the like available for AID treatment regimens. In another non-limiting example of a technological advantage, the computing technology of an AID device (including, for example, the wearable infusion device and/or a controller computing device) may be improved by being able to be controlled to implement an insulin infusion process according to some embodiments for treating T2D using the same or similar hardware (for example, conventionally used for T1D treatments). In a further non-limiting technological advantage, an AID system may operate to infuse a T2D patient with insulin using safety protocols and constraints specific for T2D patients without negatively affecting performance.

In addition, some embodiments may provide one or more practical applications of insulin infusion processes, algorithms, and/or the like described in the present disclosure. Illustrative and non-limiting practical applications may include treating diabetes, such as T2D, infusing a safe amount of insulin into a diabetic patient, operating an infusion device (for instance, an AID device) according to an insulin infusion process to provide a prescribed or predetermined amount of insulin that is safe and effective for a diabetic patient, converting conventional manual T2D information (for instance, MDI) into information configured to be used with an automated system, such as an AID system, and/or the like. Other technological advantages, improvements, and/or practical applications are provided by embodiments described in the present disclosure and would be understood by persons of skill in the art. Embodiments are not limited in this context.

In this description, numerous specific details, such as component and system configurations, may be set forth in order to provide a more thorough understanding of the described embodiments. It will be appreciated, however, by one skilled in the art, that the described embodiments may be practiced without such specific details. Additionally, some well-known structures, elements, and other features have not been shown in detail, to avoid unnecessarily obscuring the described embodiments.

In this Detailed Description, references to “one embodiment,” “an embodiment,” “example embodiment,” “various embodiments,” etc., indicate that the embodiment(s) of the technology so described may include particular features, structures, or characteristics, but more than one embodiment may and not every embodiment necessarily does include the particular features, structures, or characteristics. Further, some embodiments may have some, all, or none of the features described for other embodiments.

As used in this description and the claims and unless otherwise specified, the use of the ordinal adjectives “first,” “second,” “third,” etc. to describe an element merely indicate that a particular instance of an element or different instances of like elements are being referred to, and is not intended to imply that the elements so described must be in a particular sequence, either temporally, spatially, in ranking, or in any other manner.

illustrates an example of an operating environmentthat may be representative of some embodiments. As shown in, operating environmentmay include an insulin infusion system. In various embodiments, insulin infusion systemmay include a computing devicethat, in some embodiments, may be communicatively coupled to networkvia a transceiver. Computing devicemay be or may include a display deviceand one or more logic devices, including, without limitation, a server computer, a client computing device, a personal computer (PC), a workstation, a laptop, a notebook computer, a smart phone, a tablet computing device, a personal diabetes management (PDM) device, and/or the like. Embodiments are not limited in this context.

Insulin infusion systemmay include or may be communicatively coupled to an automatic insulin delivery (AID) deviceconfigured to deliver insulin (and/or other medication) to patient. AID devicemay be a wearable device. For example, AID devicemay be directly coupled to patient(for instance, directly attached to a body part and/or skin of the user via an adhesive and/or other attachment component).

AID devicemay include a number of components to facilitate automated delivery of insulin to patient. For example, AID devicemay include a reservoir for storing insulin, a needle or cannula for delivering insulin into the body of the person, and a pump for transferring insulin from the reservoir, through the needle or cannula, and into the body of the patient. AID devicemay also include a power source, such as a battery, for supplying power to the pump and/or other components of automatic insulin delivery device. Embodiments are not limited in this context, for example, as AID devicemay include more or less components.

AID devicemay store and provide any medication or drug to the user. In various embodiments, AID devicemay be or may include a wearable AID device. For example, AID devicemay be the same or similar to an OmniPod® device or system provided by Insulet Corporation of Acton, Massachusetts, United States, for example, as described in U.S. Pat. Nos. 7,303,549; 7,137,964; and/or 6,740,059, each of which is incorporated herein by reference in its entirety.

In some embodiments, computing devicemay be a smart phone, PDM, or other mobile computing form factor in wired or wireless communication with automatic insulin delivery device. For example, computing deviceand AID devicemay communicate via various wireless protocols, including, without limitation, Wi-Fi (i.e., IEEE 802.11), radio frequency (RF), Bluetooth™, Zigbee™, near field communication (NFC), Medical Implantable Communications Service (MICS), and/or the like. In another example, computing deviceand adjustment compliance device may communicate via various wired protocols, including, without limitation, universal serial bus (USB), Lightning, serial, and/or the like. Although computing device(and components thereof) and AID deviceare depicted as separate devices, embodiments are not so limited. For example, in some embodiments, computing deviceand AID devicemay be a single device. In another example, some or all of the components of computing devicemay be included in automatic insulin delivery device. For example, AID devicemay include processor circuitry, memory unit, and/or the like. In some embodiments, each of computing deviceand AID devicemay include a separate processor circuitry, memory unit, and/or the like capable of facilitating insulin infusion processes according to some embodiments, either individually or in operative combination. Embodiments are not limited in this context (see, for example,).

AID devicemay include or may be communicatively coupled to one or more sensors-operative to detect, measure, or otherwise determine various physiological characteristics of patient. For example, a sensor-may be or may include a CGM sensor operative to determine blood glucose measurement values of patient. In another example, a sensor-may include a heart rate sensor, temperature sensor, and/or the like.

Computing device(and/or automatic insulin delivery device) may include a processor circuitrythat may include and/or may access various logics for performing processes according to some embodiments. For instance, processor circuitrymay include and/or may access an insulin delivery logic. Processing circuitry, insulin delivery logic, and/or portions thereof may be implemented in hardware, software, or a combination thereof. The functions, processes, algorithms, and/or the like (for example, an insulin infusion process) described according to some embodiments may be performed by processor circuitry and/or insulin delivery logic(for example, via executing insulin delivery application) by computing device, automatic insulin delivery device, and/or a combination thereof. The processing circuitry, memory unit, and associated components are depicted within computing deviceto simplify(for instance, all or a portion of processing circuitry, memory unit, and associated components may be arranged within automatic insulin delivery device). Accordingly, embodiments of functionality, processes (for instance, an insulin infusion process), and/or the like described in the present disclosure with respect to computing deviceand/or components thereof may be performed in whole or in part by automatic insulin delivery device.

As used in this application, the terms “logic,” “component,” “layer,” “system,” “circuitry,” “decoder,” “encoder,” “control loop,” and/or “module” are intended to refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution. For example, a logic, circuitry, or a module may be and/or may include, but are not limited to, a process running on a processor, a processor, a hard disk drive, multiple storage drives (of optical and/or magnetic storage medium), an object, an executable, a thread of execution, a program, a computer, hardware circuitry, integrated circuits, application specific integrated circuits (ASIC), programmable logic devices (PLD), digital signal processors (DSP), field programmable gate array (FPGA), a system-on-a-chip (SoC), memory units, logic gates, registers, semiconductor device, chips, microchips, chip sets, software components, programs, applications, firmware, software modules, computer code, a control loop, a computational model or application, an AI model or application, an ML model or application, a proportional-integral-derivative (PID) controller, FG circuitry, variations thereof, combinations of any of the foregoing, and/or the like.

Although insulin delivery logicis depicted inas being within processor circuitry, embodiments are not so limited. For example, insulin delivery logicand/or any component thereof may be located within an accelerator, a processor core, an interface, an individual processor die, implemented entirely as a software application (for instance, an insulin delivery application) and/or the like.

Memory unitmay include various types of computer-readable storage media and/or systems in the form of one or more higher speed memory units, such as read-only memory (ROM), random-access memory (RAM), dynamic RAM (DRAM), Double-Data-Rate DRAM (DDRAM), synchronous DRAM (SDRAM), static RAM (SRAM), programmable ROM (PROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, polymer memory such as ferroelectric polymer memory, ovonic memory, phase change or ferroelectric memory, silicon-oxide-nitride-oxide-silicon (SONOS) memory, magnetic or optical cards, an array of devices such as Redundant Array of Independent Disks (RAID) drives, solid state memory devices (e.g., USB memory, solid state drives (SSD) and any other type of storage media suitable for storing information. In addition, memory unitmay include various types of computer-readable storage media in the form of one or more lower speed memory units, including an internal (or external) hard disk drive (HDD), a magnetic floppy disk drive (FDD), and an optical disk drive to read from or write to a removable optical disk (e.g., a CD-ROM or DVD), a solid state drive (SSD), and/or the like.

Memory unitmay store various types of information and/or applications for an insulin infusion process according to some embodiments. For example, memory unitmay store sensor information, patient information, insulin dosage information, and/or insulin delivery application. In some embodiments, sensor information, patient information, insulin dosage information, insulin delivery application, and/or portions thereof may be stored in one or more data stores-accessible to computing device(and/or automatic insulin delivery device) via network.

In some embodiments, insulin delivery applicationmay be or may include an application being executed on computing deviceand/or AID device(including a mobile application, “mobile app,” or “app” executing on a mobile device form factor). For example, in various embodiments, insulin delivery applicationmay be or may include an application the same or similar to the Omnipod® Mobile App, Glooko, Omnipod® DASH™ PDM software, and/or the like provided by Insulet Corporation of Acton, Massachusetts, United States. In addition or in the alternative, insulin delivery applicationmay be or may include an application operative to control components of automatic insulin delivery device (for instance, a pump, sensors-, and/or the like) to infuse patientwith insulin, such as an AID application. For example, insulin delivery applicationmay be or may include an AID application to monitor patient blood glucose values, determine an appropriate level of insulin based on the monitored glucose values (e.g., blood glucose concentrations and/or blood glucose measurement values) and other information, such as user-provided information, including, for example, carbohydrate intake, exercise times, meal times, and/or the like, and perform an insulin infusion process according to some embodiments to maintain a user's blood glucose value within an appropriate range.

In some embodiments, sensor informationmay include information determined via sensors-. For example, sensor informationmay include CGM information (for instance, blood glucose concentrations or other blood glucose measurement values), temperature information, heart rate information, and/or the like. In exemplary embodiments, sensor informationmay include historical information, for instance, historical blood glucose values of patient. In various embodiments, patient informationmay include information associated with patient. Non-limiting examples of patient informationmay include demographic information, physical information (for instance, height, weight, and/or the like), diabetes condition information (for instance, type of diagnosed diabetes (T1D or T2D)), insulin needs (for instance, MDI information, TDI information, insulin types, and/or the like), activity information (for instance, meals and/or meal times, carbohydrate intake, exercise information, and/or the like), insulin sensitivity information, and/or the like. In some embodiments, at least a portion of patient informationmay be manually entered by patientor a caregiver, for example, via a user interface of insulin delivery application. In some embodiments, patient informationmay include historical information, such as historical values associated with mealtimes, carbohydrate intake, exercise times, and/or the like.

In some embodiments, insulin dosage informationmay include information used to perform an insulin infusion process via AID deviceaccording to some embodiments. Non-limiting examples of insulin infusion information may include MDI information, TDI information, CGM information, basal dosage information, basal rate information, basal parameter(s), current glycated hemoglobin (HbA1c or “A1C”) information, target A1C information, blood glucose difference (for instance, BG), correction factor parameter(s) (CF), maximum additional insulin delivery information (I), AID process or algorithm information, safety constraint information, adjustment factor(s) (F), thresholds (for example, total insulin delivery during a certain duration, insulin-on-board (IOB), IOB decay rate (D(t)), and/or the like), change in basal (for example, Ab(i)), insulin sensitivity factors, constants, tunable parameters, and/or the like.

Insulin delivery logic, for example, implemented via insulin delivery applicationbeing executed by processor circuitry, may operate to perform an insulin infusion process according to some embodiments to infuse a patient with insulin. In various embodiments, the insulin infusion process may be configured for a T2D patient.

In some embodiments, the insulin infusion process may operate to determine insulin dosage informationin the form of a basal parameter for patient. The basal parameter may be used by the insulin infusion process, for example, via an associated AID algorithm, to deliver a dosage of insulin to patient. For example, the basal parameter may be or may be used for determining continuous insulin infusion (CII) rate. A non-limiting example of a CII may be or may include continuous subcutaneous insulin infusion (CSII) information. However, embodiments are not limited to subcutaneous infusion (for instance, insulin delivery may occur at the dermal layer of the skin of patient). In some embodiments, the basal parameter may be the basal insulin needs of patient.

In some embodiments, insulin delivery logicmay implement the insulin infusion process to convert MDI insulin quantities of patientinto CII (or CSII) for use in an AID algorithm. For example, T2D patients may utilize one or more daily injections (MDI) of insulin per day of a fixed quantity of (long-acting) insulin for each injection. For instance, patientmay be a T2D patient having an MDI protocol requiring two injections of 20 units (U) of long-acting insulin, for a TDI total of 40 U per day. In various embodiments, the total amount of insulin (TDI or total daily dosage (TDD)) to be delivered may be converted into a basal parameter. For example, in some embodiments, it may be assumed that T2D patients often do not bolus for their meals such that the entirety of the insulin deliveries each day can be considered to be their basal needs, instead of the typical assumption of utilizing 50% of the total insulin deliveries as their basal needs (for instance, for T1D patients used in conventional AID algorithms).

The following Equation (1) may be used to determine TDI based on MDI:

where MDIis the ninjection of patient. The following Equation (2) may be used to determine the basal parameter based on TDI:

In various embodiments, the basal parameter may be used as input to an AID algorithm, for example, to determine an infusion rate, total infusion dosage, and/or the like. In Equation (2), the value 24 may indicate a 24-hour period for the basal parameter. Other values may be substituted for other time periods (for instance, 48 for ½ hour time periods, 144 for 10-minute time periods, 288 for 5-minute time periods, and so on). For example, for a patient having a TDI of 45 units, the basal parameter may be about 1.875 units/hour for an AID algorithm infusion rate.

In some embodiments, the insulin infusion process may determine a maximum daily dosage of insulin. In various embodiments, the maximum daily dosage may be determined based, at least in part, on target health conditions for patient. For example, in one embodiment, the maximum daily dosage may be determined based on a target A1C for patient. A1C is an important factor in assessing the quality of blood glucose control for people with T2D. In general, A1C is a percentage indicating patient blood sugar levels over a previous time period, typically two to three months. A normal A1C (for instance, of a non-diabetic individual) may be a value less than about 6.0%. A target A1C for T2D patients may be about 7.0%; however, each patient may have their own target A1C. Diabetes management of people with T2D often have a target A1C, for example, that is lower than their current A1C.

In some embodiments, the difference between the current A1C and the target A1C may be converted into a difference in mean blood glucose (BG) according to the following Equation (3):

In various embodiments, the basal needs of patient(for instance, the basal parameter) may be used to estimate a correction factor (CF) parameter (for instance, for T2D patients). In some embodiments, CF may be determined by converting the basal parameter to daily needs using an insulin sensitivity factor. In some embodiments, the insulin sensitivity factor may be the “1800 rule” or other similar sensitivity factor for indicating patient reactions to insulin (for instance, the 1800 rule may be used to determine how much a patient's blood sugar may drop for each unit of a particular type of insulin by dividing 1800 by the number of units of insulin delivered over an infusion time period). In various embodiments, each patient may have their own sensitivity factor. For example, in an embodiment, CF may be determined for the 1800 rule according to the following Equation (4):

where 24 indicates 24 hours per day (an infusion time period) and the 1800 (infusion sensitivity factor) may be substituted based on the particular insulin sensitivity used to determine CF.

In exemplary embodiments, the insulin infusion process may use the difference in mean BG (for instance, BG) to determine an expected total additional insulin need per day (I) according to the following Equation (5):

where the value 6 is a tunable cycle parameter configured to represent the number of 4-hour cycles of insulin peak times per 24-hour period. Accordingly, Equation (5) may use different cycle parameter values other than 6 depending on, for instance, the specific treatment regimen and/or physiological characteristics of patient. In some embodiments, Imay be insulin to be delivered to a patient in addition to the volume indicated by the basal parameter (for instance, bolus insulin requirements) to achieve diabetic therapy goals for patient.

In various embodiments, Imay be set as the maximum additional delivery of insulin allowed for patient(for instance, the maximum allowed beyond TDI or basal). In some embodiments, Imay be set as the maximum additional delivery of insulin allowed for patientwithin a basal parameter time period (for instance, 24-hour time period) by AID device. For example, in various embodiments, the actual insulin delivery by AID devicemay be defined according to the following Equation (6):

where t is the current control cycle where the actual insulin delivery decision may be made, T is the time unit of consideration to apply this limit (for instance, 1 for hour 1, 2 for hour 2, and so on), I(t) is the amount of insulin to be infused into patientat time t, AID(t) is the amount of insulin determined for infusion according to the AID algorithm associated with AID deviceat time t, and basal(t) is the basal units to be delivered at time t (for example, if basal is 30 units per day and t is 24 (for instance, infusion every hour), then basal(t) is 30/24=1.25). With reference to Equation (6), delivery of insulin according AID(t) may occur if the maximum delivery limit has not been reached, and basal (b(t)) may be delivered if the maximum delivery limit has been reached. For example, Imay be or may be considered AIDif the sum of differences between AID-basal is less than the threshold, and basal if AID-basal is greater than the threshold. In some embodiments, the maximum delivery limit may be determined based on (basal parameter)+I. Accordingly, in some embodiments, the insulin infusion process may use Equation (6) to limit the automated insulin delivery to basal in a binary manner by comparing against the additional insulin delivery (I).

In one exemplary embodiment, the threshold for maximum insulin delivery allowed beyond the user's basal per 24 hour period can be set to 6 hours (T=6). In this example, defining T=6 may mean that the system cannot deliver more than 6 times basal, above the user's basal, in total, over the last 6 hours. Thus, at any cycle t, the system may take the sum of insulin deliveries in the last 6 hours, corresponding to the first term in the summation portion of Equation 6. In this example, for instance, the total delivery may be 15 U of insulin and basal may be 2 U/hour. Then, the sum of the total insulin delivery minus the total basal in the last 6 hours is 3 U. This is less than 12U, which is the stated maximum insulin delivery allowed above basal in this example. As a result, the system would deliver the full insulin delivery request as defined by AID(t) for the current cycle t. On the other hand, if the total delivery was 25 U, the user received 13 U above basal, which exceeds the stated threshold of 12 U. In this case, the system would deliver the basal value b(t) instead of AID(t) in the current cycle t.

In various embodiments, the insulin infusion process may include determining one or more safety constraints or safety constraint adjustments. In some embodiments, the safety constraint adjustments may be based, at least in part, on additional insulin delivery (I) requirements. In exemplary embodiments, the safety constraints may be adjusted to allow a continuous limitation of AID deviceinsulin delivery that still allows for delivery of an amount of insulin beyond Ias necessary.

Specifically, safety constraints within an AID algorithm may be associated with, at least in some form, to the patient TDI. For example, because Imay represent the additional total insulin delivery, the safety constraints may be adjusted (for example, relaxed) by an adjustment factor. For instance, the safety constraints may be relaxed by a percentage adjustment (F) based on the proportion of Iversus the user's TDI, according to the following Equation (7):

In some embodiments, the insulin infusion process may include a duration maximum threshold (or integral delivery) safety constraint. For example, the duration maximum factor may provide that the total insulin delivery for patientduring a certain duration (for example, 3 hours) cannot exceed a tunable factor (for instance, 9) times the basal parameter. The adjustment factor can affect (for example, increase) the duration maximum threshold, for instance, by multiplying the duration maximum threshold by Fas provided in the following Equation (8):

where 9 is the tunable factor and the value 48 may be modified based on the duration of interest.